Dedicated GPU · NVIDIA T4 & A10G · Flat monthly rate
One physical NVIDIA T4 (16 GB VRAM) or A10G (24 GB VRAM), exclusively yours, running 24/7 from a flat $499/month (T4 ₹48,403 · A10G $999 / ₹96,903 in India, no GST). Never shared, never spot, never interrupted — and never metered by the hour.
Flat monthly rate · Cancel anytime · One physical GPU per subscription · CUDA pre-installed
Most "GPU cloud" offers time-slice, virtualize, or spot-bid the hardware. SnapDeploy's NVIDIA T4 hosting gives you the whole card on a dedicated on-demand instance — the GPU equivalent of a private office, not a hot desk.
A physical T4 on a dedicated on-demand g4dn.xlarge-class instance with 4 vCPUs and 16 GB of system RAM.
Not shared, not virtualized, not partitioned. torch.cuda.is_available()
returns True out of the box.
Your model weights stay loaded in VRAM around the clock. No cold starts, no wake-up delay, no hourly metering. The first request of the day is as fast as the thousandth.
Dedicated on-demand capacity is never reclaimed mid-inference. No spot evictions taking your API down mid-request, no capacity lotteries when demand spikes.
The bill never changes, no matter the traffic. A runaway retry loop or a viral day cannot change what you pay. Cancel anytime — the subscription stops immediately.
Two dedicated tiers, one flat-rate model. Both are physical GPUs, exclusively yours, running 24/7.
₹48,403/mo in India, no GST · g4dn.xlarge-class instance, 4 vCPU
₹96,903/mo in India, no GST · g5.xlarge-class instance, 4 vCPU
The subscription buys the GPU and the platform around it. You bring a repository; SnapDeploy does the rest. There is no IAM to configure, no VPC to design, no CUDA AMI to maintain, and no deploy pipeline to build — push to GitHub and your GPU container rebuilds and redeploys automatically.
Docker containers & raw source code
Deploy any CUDA workload from GitHub — SnapDeploy scans your dependencies, picks the right CUDA base image, and builds the Docker image for you. No CUDA driver installs, no GPU runtime config.
One-click AI templates
Pre-built PyTorch, TensorFlow, Hugging Face, and ONNX Runtime templates with CUDA and FastAPI pre-configured — live inference endpoints in 2-3 minutes. See the templates guide.
Domains, TLS & live logs
Every GPU container gets a public HTTPS URL with free TLS, custom domain support, live logs, and monitoring from one dashboard. Production plumbing included, not bolted on.
There is exactly one number to know: $499/month (₹48,403 in India, no GST). No per-second billing, no credits to top up, no forgotten instances quietly burning money overnight.
≈ $0.68/hr effective over ~730 hours of 24/7 runtime — fully managed.
The raw instance is cheaper per hour — but you become the platform team.
The ~$0.15/hr spread is what "fully managed" costs: SnapDeploy provisions, patches, routes, monitors, and redeploys the GPU for you. If your time is worth anything, the DIY rate is not actually cheaper. Full breakdown in our GPU cloud pricing guide.
Anything that speaks CUDA. The T4's 16 GB of VRAM comfortably serves most production inference workloads:
SnapDeploy scans your dependency files before every build and blocks common mistakes early — GPU packages headed for a CPU container, or a GPU container with nothing that needs CUDA. It also auto-installs system dependencies your framework needs, like ffmpeg for Whisper or libsndfile for librosa.
Deploy AI models in three steps:
Step-by-step walkthrough with code examples in our guide to deploying AI models on GPU cloud containers.
Eight open-source templates that deploy from the gallery in 2-3 minutes — LLM serving, image generation, speech-to-text, and classic ML frameworks. Pick one, click Deploy, get a live HTTPS URL.
Ollama
Self-hosted LLM server for Llama, Mistral & Qwen — set OLLAMA_MODEL, models pull in the background (port 11434). Ollama hosting guide.
vLLM
High-throughput OpenAI-compatible /v1/chat/completions serving — swap in any Hugging Face model via an env var.
ComfyUI / SDXL
Node-based Stable Diffusion XL — the checkpoint downloads in the background on first start (port 8188). Stable Diffusion hosting guide.
Whisper
Self-hosted speech-to-text API on faster-whisper large-v3 — POST audio to /transcribe.
PyTorch
PyTorch + CUDA 12.1 + FastAPI — serve any .pt/.pth model as a REST API.
Hugging Face
Transformers + Accelerate — load any Hugging Face Hub model by changing one line.
TensorFlow
TensorFlow 2.x with CUDA — Keras and SavedModel serving with GPU acceleration out of the box.
ONNX Runtime
ONNX + TensorRT — optimized production inference, often 2-5x faster than the native framework.
All templates are open source on GitHub — fork one, swap in your model, deploy the fork. Deep dives: Ollama LLM hosting, ComfyUI / Stable Diffusion hosting, and the framework templates guide.
Monthly cost of running a T4-class GPU 24/7, side by side. Competitor prices are approximate, based on published rates as of September 2026 — check their pricing pages for current numbers.
| Platform | ~Monthly (24/7) | Billing model | What you can run |
|---|---|---|---|
| SnapDeploy Dedicated GPU (T4) | $499 flat | Flat monthly, dedicated physical T4 | Any Docker container or GitHub source repo — APIs, UIs, batch jobs, full apps |
| SnapDeploy Dedicated GPU (A10G) | $999 flat | Flat monthly, dedicated physical A10G (24 GB VRAM) | Any Docker container or GitHub source repo — APIs, UIs, batch jobs, full apps |
| Hugging Face Endpoints (T4) | ~$365 | Per-hour, managed endpoint | Model inference endpoints only |
| Baseten (dedicated) | ~$460 | Per-minute, dedicated deployment | Model serving only |
| Northflank (L4) | ~$584 | Per-hour, containers | Containers (L4-class GPU) |
| Replicate | ~$591 equivalent | Per-prediction / per-second | Hosted model predictions |
| Modal | ~$800 equivalent | Per-second serverless compute | Python functions and jobs |
The honest read: cheaper T4-class rates exist if you only need bursts, and some rivals undercut $499 for pure inference endpoints. SnapDeploy's difference is scope — a dedicated physical GPU that is exclusively yours, running any Docker container 24/7, with domains, TLS, logs, and GitHub deploys included, at a price that cannot surprise you.
Honesty section. Skip this part if you like surprises.
Training large models. The T4 is an inference and light fine-tuning card. Training 7B+ parameter LLMs or running Stable Diffusion XL at full resolution wants more VRAM than 16 GB — that is what the NVIDIA A10G tier (24 GB VRAM, $999/month) is for. Truly large training runs still belong on multi-GPU clusters.
Short bursts. If your workload is a one-off experiment measured in single hours, a per-second provider will be cheaper for that burst. Flat-rate dedicated GPU hosting pays off when the GPU works for a living — production APIs, always-warm inference, steady pipelines.
CPU-sized workloads. If your app doesn't use CUDA, you don't need this. SnapDeploy's CPU containers deploy free (10 deploys/day) with Always-On from $12/month — see full pricing.
Your choice of two tiers: a physical NVIDIA T4 with 16 GB of VRAM on a dedicated on-demand g4dn.xlarge-class instance ($499/month), or a physical NVIDIA A10G with 24 GB of VRAM on a dedicated g5.xlarge-class instance ($999/month). Both have 4 vCPUs, CUDA pre-installed, and run 24/7 with no auto-sleep and no hourly metering.
Really dedicated. Each subscription is backed by one physical GPU (T4 or A10G, per your tier) — not shared with other tenants, not virtualized or partitioned, and not spot capacity that can be reclaimed mid-inference.
Any Docker container or raw source code that uses NVIDIA CUDA — PyTorch, TensorFlow, Hugging Face Transformers, ONNX Runtime, Whisper, Stable Diffusion, vLLM, Gradio, Streamlit, and more. Push a GitHub repository and SnapDeploy builds the image for you (no Dockerfile needed), bring your own Docker image, or use the one-click templates and be live in 2-3 minutes.
Replicate bills per prediction and Modal bills per second — costs scale with usage and can reach roughly $590-$800/month for T4-class capacity running 24/7 (approximate, as of September 2026). SnapDeploy is a flat $499/month for a GPU that is exclusively yours, running any Docker container — not just model endpoints.
₹48,403 per month for the T4 tier and ₹96,903 per month for the A10G tier, no GST added — the Indian-rupee equivalents of the flat $499 and $999 monthly rates. Same dedicated NVIDIA GPU, same 24/7 runtime, cancel anytime.
The T4 tier ($499/month) gives you 16 GB of VRAM — ideal for production inference, most Hugging Face models under ~3B parameters, and Whisper. The A10G tier ($999/month) gives you 24 GB of VRAM on a newer Ampere-generation GPU — for larger models, heavier fine-tuning, and Stable Diffusion XL-class workloads. Both are dedicated physical GPUs at a flat monthly rate.
Yes. Cancel from your billing page at any time — cancellation takes effect immediately and your GPU container is stopped. Your code and container configuration are preserved, so you can subscribe again later and redeploy without starting over.
One physical NVIDIA T4 or A10G. From a flat $499/month. Running 24/7, exclusively for you.
Cancel anytime · T4 ₹48,403/mo · A10G ₹96,903/mo in India, no GST · See full pricing